With the goal of determining genetic and epidemiological factors in age-related traits, we are applying new statistical approaches to look for covariates and hidden correlations in large population data sets. A particular focus is the NIA-sponsored study of the founder Sardinian population, where inter-relatedness and stable environment of the population over many generations can simplify the analysis. The study has been scoring >200 dichotomized traits (smoking, etc.) and 98 quantitative traits ("endophenotypes" or "quantitative risk-related genetic or environmental factors") that can be scored on a continuous scale. The use of quantitative traits permits the study of the entire range of allelic variation in a population. Traits of special interest include a range of cardiovascular risk factors, anthropometric measurements, blood test values, and facets of personality. In a current 5-year contract, a team of Sardinian scientists has recruited over 6,100 subjects from a selected group of four towns in east-central Sardinia, and has measured all traits for each subject. The sample cohort numbers over half of the population of the region aged 14-102; they are native-born, and at least 96 percent are known to have all grandparents born in the same province. The group include 4933 phenotyped sib pairs, 4266 phenotyped parent-child pairs, >4069 phenotyped cousin pairs, and more than 6459 phenotyped avuncular pairs. This sample is large enough and well enough phenotyped to show that even in this founder population, the variance for individual traits is comparable to that in outbred populations; and it is large enough and interrelated enough to infer highly significant estimates of genetic heritability for traits. Genome-wide scans (genotyping) should have the power to detect loci that contribute the order of 10 percent of variance for a trait. With this cohort, full-genome scans with batteries of single-nucleotide markers are being carried out. In addition, second visits have been initiated for the study cohort to permit the assessment of longitudinal trends and outcomes, as well as the assessment of additional phenotypes related to bone density and frailty as a function of age. While the genome scans of the population begin to search for genes involved in the determination of particular traits, targeted data analysis is being done in this subproject to look for correlated/overlapping genetic and epidemiological factors, including unexpected correlations, and to compare possible correlations with other large population cohort studies, including the Baltimore Longitudinal Study of Aging.